It is time to develop the Group’s activities to be more supported and extended to new horizons. Aiming at expanding this work, after about six years of launching, and after paying sincere time and effort with physical and intellectual altruism, we can declare that the entity has the maturity and size to conquer any limits. In addition, from our accumulated experience over the past years, we observed and recognized the deliberate and artificial hurdles and problems. In retrospect, we
have developed stronger motivation towards more success and distinction.

Moving up:

from the narrow closets to the spaciousness of the Internet,

from the limited visibility to innovative capacity,

and from restrictive bureaucracy to the freedom and hopes of research and development.

And for achieving these foreseen prospective, We announce the launch of the activity of virtual lab of Human Computer Interaction (Virtual HCI Lab.) with the same principles that we started out, with the same group, and–more broadly in terms of activity and communication–with relevant research activity in the same areas.

Very successful HCI Lab. Annual Summer Training:
———————————————————————
It is with great pleasure that we announce the successful completion of the 2017 #HCI_Lab summer internship program. Out of more than 30 applicants, 15 students made it through six weeks of #multidisciplinary#research intensive tracks. The tracks spanned a wide spectrum of #Computer_Science areas such as: Human-Computer Interaction #HCI, Internet of Things #IoT, #Data_Science and #Machine_Learning, #Cryptography, #Image_Processing and Brain-Computer Interfaces #BCI. Each successful student is offered the opportunity to further pursue research with one of the lab’s professors building towards publishing a student #paper. We are hoping to see more high-caliber enthusiastic students in our upcoming cycles for the coming years, God willing.

This project applies automatic methods for classification and recognition of urine analysis microscopic images. We found that, it is necessary to apply automatization in the field of microscopic analyses of urine solution as detecting particles in the microscopic image is repeated and time consuming. Furthermore, Particles in many medical laboratory analyses have irregular shapes and blur edges. This project applies medical image processing algorithms and pattern recognition to microscopic images. It is composed of three stages: first, original urinary sediment microscopic images are transformed into binary image by image preprocessing including median filtering, color image conversion to gray scale image and image segmentation. Second, we select and extract some objects from images. Third, we classify the extracted images using SVM to recognize four kinds of urine sediment components: red blood cells, white blood cells, cast, calcium oxalate.